强P-正则半群的一个构造

本文研究了强P-正则半群的结构.利用正则*-半群和一族满足某种条件的映射给出了强P-正则半群的一个构造,推广和丰富了相关文献中纯正半群的结果.     

无界抽样情形下不定核的系数正则化回归

本文讨论样本依赖空间中无界抽样情形下最小二乘损失函数的系数正则化问题. 这里的学习准则与之前再生核Hilbert空间的准则有着本质差异: 核除了满足连续性和有界性之外, 不需要再满足对称性和正定性; 正则化子是函数关于样本展开系数的l²-范数; 样本输出是无界的. 上述差异给误差分析增加了额外难度. 本文的目的是在样本输出不满足一致有界的情形下, 通过l²-经验覆盖数给出误差的集中估计(concentration estimates). 通过引入一个恰当的Hilbert空间以及l²-经验覆盖数的技巧, 得到了与假设空间的容量以及与回归函数的正则性有关的较满意的学习速率.     

油藏数值模拟中动边值问题的特征差分方法

油气运移、聚集史软件的功能是重建盆地发育中油气资源运移、聚集的历史,它对于合理评估油气资源的勘探和开发有重要的价值。其数学模型是一组非线性偶合偏微分方程组的动边值问题。通过一类特征差分格式,得到了最佳阶的l~2误差估计结果。对这一领域的模型分析、数值方法和软件研制均有重要的理论和实用价值。     

离散时间观测下的α-赋权分数桥的参数估计

本文研究了赋权分数桥dX_t=-α(X_t)/(T-t)dt+dB_t^a,b,0≤t < T,中未知参数α>0的参数估计问题,其中B^a,b是参数为a>-1,|b|<1,|b|≤a+1的赋权分数布朗运动.假设对随机过程X_t进行离散观测t_i=i△_n,i=0,…,n,且T_n=n△_n.本文构造了α的最小二乘估计α_n,证明了当n→∞时,α_n依概率收敛到α.     

基于加权复杂网络的文本关键词提取

通过分析基于复杂网络的网络提取的文本关键词新算法.首先根据文本特征词之间的关系构建文本的加权复杂网络模型,其次通过节点的加权聚类系数和节点的介数计算节点的综合特征值,最后根据综合特征值提取出文本关键词.实验结果表明,该算法提取的关键词能够较好地体现文本主题,提取关键词的准确率比已有算法有明显提高.     

动载荷识别的非迭代法研究

为了快速准确地识别结构在复杂环境下的承载状态，基于有限元法和Newmark-β法提出了一种非迭代反演方法，并用于识别结构上施加的动载荷.通过探寻测量信息与待演参量之间的关系，建立误差函数，根据最小二乘法实现动载荷的直接识别无需迭代，其中对待反演的分布载荷实施基函数展开，以提高算法的抗不适定性.同时奇异值分解法被用来求解病态方程组.数值算例分别讨论了测量噪声、测点数量、基函数展开、测点位置和不同时间步长对反演结果的影响，结果显示该方法在识别动载荷时具有较高的精度和效率.     

近切触流形的φ*-解析向量场

本文引入了近切触流形（M,ø,ξ,η,g）中φ^*-解析向量场的概念,并研究了其性质.利用近切触流形的性质,证明了切触度量流形中的φ^*-解析向量场v是Killing向量场且φv不是φ^*-解析的.特别地,如果近切触流形M是正规的,得到v与ξ平行且模长为常数.另外,证明了3维的切触度量流形不存在非零的φ^*-解析向量场.     

识别Rayleigh-Stokes方程源项的分数阶Landweber迭代正则化方法

该文研究具有Riemann-Liouville时间分数阶导数的Rayleigh-Stokes方程未知源识别问题.首先证明这个问题是不适定的,并应用分数阶Landweber正则化方法求解此反问题.基于条件稳定性结果,在先验和后验正则化参数选取规则下,分别给出精确解与正则解之间的误差估计.最后通过数值例子说明此方法求解此类...     

偏最小二乘回归的贝叶斯正则化神经网络集成模型在证券分析预测中的应用

神经网络集成技术能有效地提高神经网络的预测精度和泛化能力,已经成为机器学习和神经计算领域的一个研究热点.利用Bagging技术和不同的神经网络算法生成集成个体,并用偏最小二乘回归方法从中提取集成因子,再利用贝叶斯正则化神经网络对其集成,以此建立上证指数预测模型.通过上证指数开、收盘价进行实例分析,计算结果表明该方法预测精度高、稳定性好.     

用牵制控制方法进行含随机噪声的复杂网络的拓扑结构识别

本文研究了含噪声的复杂动态网络的拓扑结构识别的问题.利用牵制控制(Pinning控制)方法,基于随机微分方程的理论基础,来进行网络的拓扑结构识别,设计自适应反馈控制器和识别率,来反演网络结构,通过数值仿真,获得了pinning控制方法主要是通过一部分未知节点来识别整个网络的拓扑结构的结果,噪声强度的范围将影响网络结构正确识别时的耦合强度范围,噪声强度越大,可识别的耦合强度也越大.     

THE FUNDAMENTAL GROUP OF THE AUTOMORPHISM GROUP OF A NONCOMMUTATIVE TORUS

Assume that each completely irrational noncommutative torus is realized as an inductive limit of circle algebras, and that for a completely irrational noncommutative torus Aw of rank m there are a completely irrational noncommutative torus Aρ of rank m and a positive integer d such that tr(Aw)=1/d.tr(Aρ).It is proved that the set of all C^*-algebras of sections of locally trivial C^*-algebra bundles over S^2 with fibres Aω has a group sturcture,denoted by π1^s(Aut(Aω)),which is isomorphic to Zif Ed&gt;1 and {0} if d&gt;1.Let Bcd be a cd-homogeneous C^*-algebra over S^2&#215;T^2 of which no non-trivial matrix algebra can be factored out.The spherical noncommutative torus Sρ^cd is defined by twisting C^*(T2&#215;Z^m-2) in Bcd &#215;C^*(Z^m-3) by a totally skew multiplier ρ on T^2&#215;Z^m-2。It is shown that Sρ^cd&#215;Mρ∞ is isomorphic to C(S^2)&#215;C^*(T^2&#215;Z^m-2,ρ）&#215; Mcd(C)&#215;Mρ∞ if and only if the set of prime factors of cd is a subset of the set of prime factors of p.     

高阶复线性微分方程解的角域增长性

本文研究了高阶复线性微分方程解在角域上的增长性问题.利用Nevanlinna理论和共形变换的方法,获得了一些使得方程非平凡解在角域上有快速增长的系数条件,这些结果丰富了复方程解在角域上增长性的研究.     

容有半对称度量联络的广义复空间中子流形上的Chen-Ricci不等式

本文研究了容有半对称度量联络的广义复空间中的子流形上的Chen-Ricci不等式.利用代数技巧,建立了子流形上的Chen-Ricci不等式.这些不等式给出了子流形的外在几何量-关于半对称联络的平均曲率与内在几何量-Ricci曲率及k-Ricci曲率之间的关系,推广了Mihai和Özgür的一些结果.     

Uniformly non-l n (1) Musielak-Orlicz sequence spaces

We give a necessary and sufficient condition for the uniformly non-l _n ⁽¹⁾ property of Musielak-Orlicz sequence spacesl ^Φ generated by a sequence Φ=(ϕ_n:n⩾l) of finite Orlicz functions such that for eachn∈ℕ. As a result, forn ₀⩾2, there exist spacesl ^Φ which are only uniformly non-l _n ⁽¹⁾ forn⩾n ₀. Moreover we obtain a characterization of uniformly non-l _n ⁽¹⁾ and reflexive Orlicz sequence spaces over a wide class of purely atomic measures and of uniformly non-l _n ⁽¹⁾ Nakano sequence spaces. This extends a result of Luxemburg in [19]. Submitted in memory of Professor W. Orlicz     

A New Construction of Central Relative (p a , p a , p a , 1)-Difference Sets

Semiregular relative difference sets (RDS) in a finite group E which avoid a central subgroup C are equivalent to orthogonal cocycles. For example, every abelian semiregular RDS must arise from a symmetric orthogonal cocycle, and vice versa. Here, we introduce a new construction for central (p ^a , p ^a , p ^a , 1)-RDS which derives from a novel type of orthogonal cocycle, an LP cocycle, defined in terms of a linearised permutation (LP) polynomial and multiplication in a finite presemifield. The construction yields many new non-abelian (p ^a , p ^a , p ^a , 1)-RDS. We show that the subset of the LP cocycles defined by the identity LP polynomial and multiplication in a commutative semifield determines the known abelian (p ^a , p ^a , p ^a , 1)-RDS, and give a second new construction using presemifields.We use this cohomological approach to identify equivalence classes of central (p ^a , p ^a , p ^a , 1)-RDS with elementary abelian C and E/C. We show that for p = 2, a 3 and p = 3, a 2, every central (p ^a , p ^a , p ^a , 1)-RDS is equivalent to one arising from an LP cocycle, and list them all by equivalence class. For p = 2, a = 4, we list the 32 distinct equivalence classes which arise from field multiplication. We prove that, for any p, there are at least a equivalence classes of central (p ^a , p ^a , p ^a , 1)-RDS, of which one is abelian and a – 1 are non-abelian.     

基于函数型数据的系数正则化回归的收敛速度

本文研究了基于函数型输入和₁-正则化的最小二乘回归问题的推广性能.利用基于Rademacher平均的分析技术,获得了学习速度的估计,推广了已有的欧式空间有限维输入结果.     

An algorithm for fitting circular arcs to data using the <Emphasis Type="Italic">l</Emphasis><Subscript><Emphasis Type="Italic">1</Emphasis></Subscript> norm

There are many applications of fitting circular arcs to data. We have for example, system control, using a computer controlled cutting machine, approximating hulls of boats, drawing and image techniques. Out of these applications comes the least squares norm to be the most commonly used criterion. This paper examines how the l ₁ norm is used which seems to be more appropriate than the use of least squares in the context of wild points in the data. An algorithm and different methods to determine the starting points are developed. However, numerical examples are given to help illustrate these methods.      

Mixed sensitivity minimization problems with rationall 1-optimal solutions

Up to this time, the only known method to solve the discrete-time mixed sensitivity minimization problem inl ¹ has been to use a certain infinite-dimensional linear programming approach, presented by Dahleh and Pearson in 1988 and later modified by Mendlovitz. That approach does not give in general true optimal solutions; only suboptimal ones are obtained. Here, for the first time, the truel ¹-optimal solutions are found for some mixed sensitivity minimization problems. In particular, Dahleh and Pearson construct an 11h order suboptimal compensator for a certain second-order plan with first-order weight functions; it is shown that the unique optimal compensator for that problem is rational and of order two. The author discovered this fact when trying out a new scheme of solving the infinite-dimensional linear programming system. This scheme is of independent interest, because when it is combined with the Dahleh-Pearson-Mendlovitz scheme, it gives both an upper bound and a lower bound on the optimal performance; hence, it provides the missing error bound that enables one to truncate the solution. Of course, truncation is appropriate only if the order of the optimal compensator is too high. This may indeed be the case, as is shown with an example where the order of the optimal compensator can be arbitrarily high.